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Erschienen in:
High-Power Propulsion Strategies for Aquatic Take-off in Robotics Kapitel lesen Erstes Kapitel lesen

2018 | OriginalPaper | Buchkapitel

Using Geometry to Detect Grasp Poses in 3D Point Clouds

verfasst von : Andreas ten Pas, Robert Platt

Erschienen in: Robotics Research

Verlag: Springer International Publishing

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Abstract

This paper proposes a new approach to using machine learning to detect grasp poses on novel objects presented in clutter. The input to our algorithm is a point cloud and the geometric parameters of the robot hand. The output is a set of hand poses that are expected to be good grasps. There are two main contributions. First, we identify a set of necessary conditions on the geometry of a grasp that can be used to generate a set of grasp hypotheses. This helps focus grasp detection away from regions where no grasp can exist. Second, we show how geometric grasp conditions can be used to generate labeled datasets for the purpose of training the machine learning algorithm. This enables us to generate large amounts of training data and it grounds our training labels in grasp mechanics. Overall, our method achieves an average grasp success rate of 88% when grasping novels objects presented in isolation and an average success rate of 73% when grasping novel objects presented in dense clutter. This system is available as a ROS package at http://​wiki.​ros.​org/​agile_​grasp.

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Vorheriges Kapitel Synthesis and Optimization of Force Closure Grasps via Sequential Semidefinite Programming
Nächstes Kapitel Grasping with Your Brain: A Brain-Computer Interface for Fast Grasp Selection
Fußnoten
1
A friction cone describes the space of normal and frictional forces that a point contact with friction can apply to the contacted surface [13].
 
2
Any frame aligned with the surface normal is a Darboux frame. Here we restrict consideration to the special case where it is also aligned with the principal directions.
 
3
Taubin’s method is an analytic solution that performs this fit efficiently by solving a generalized Eigenvalue problem on two \(10\times 10\) matrices [18]. In comparison to using first order estimates of surface normal and curvature, the estimates derived from this quadratic are more robust to local surface discontinuities.
 
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Taubin, G.: Estimation of planar curves, surfaces and nonplanar space curves defined by implicit equations, with applications to edge and range image segmentation. IEEE Trans. PAMI 13, 1115–1138 (1991)CrossRef
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Metadaten
Titel
Using Geometry to Detect Grasp Poses in 3D Point Clouds
verfasst von
Andreas ten Pas
Robert Platt
Copyright-Jahr
2018
Buch
Robotics Research

Print ISBN: 978-3-319-51531-1

Electronic ISBN: 978-3-319-51532-8

Copyright-Jahr: 2018

DOI
https://doi.org/10.1007/978-3-319-51532-8_19

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